Intelligence
Intelligential Entropy Foreword What I hope to achieve with this chapter, is to increase the popularity of a certain concept resolving the definition of intelligence, and as far as I know, there doesn’t seem to be much of a consensus on the definition of intelligence. Some people think IQ is equal to intelligence, and others think it’s just a part of general intelligence. According to the latter, IQ tests have a g loading (the portion of the whole equation) of around .80. Other examples of g loadings are how intelligent other people rate you, academic achievements, etc. I, however, found a rather interesting way of defining intelligence, namely correlating or even defining it through entropy. It all started with the Alexander Wissner-Gross formula. Alexander Wissner-Gross formula The formula looks as follows: F = T ∇ Sτ. From: https://en.wikipedia.org/wiki/Intelligence#Definitions “Intelligence is a force, F, that acts so as to maximize future freedom of action. It acts to maximize future freedom of action, or keep options open, with some strength T, with the diversity of possible accessible futures, S, up to some future time horizon, τ. In short, intelligence doesn’t like to get trapped”. Intelligence formula and computer example An easy way to give an example is through a computer. Let’s say you buy a computer A with a certain CPU, which can do 3 billion instructions per second, then T = 3E9. Now let’s say computer A is playing a game of chess against computer B where T = 2E9. If we keep ourselves to these numbers, then the probability that computer A will win is higher than computer B. However, if we bring ∇ Sτ into the equation, we might get much different results. Let’s say, for example, that computer B is much more ‘patient’ and will take 2 seconds for each chess movement, while computer B only takes 1 second for every chess movement. Then F in computer A becomes F = 3E9 x 1 = 3E9, while that of computer B becomes F = 2E9 x 2 = 6E9, a number higher than computer A. So now the roles are reversed, computer B has a higher probability to win than computer A. Now, it’s much more complicated than this, but I suggest reading this if you are interested in more depth: https://michaelscharf.blogspot.com/2014/02/a-new-equation-for-intelligence-f-t-s.html — A new Equation for Intelligence F = T ∇ Sτ — a Force that Maximises the Future Freedom of Action How to increase F in real life? Certain things we can learn from this formula and the above example are: # That certain habits like having patience can certainly increase F, or in other words, intelligent decision-making; # Knowing how our brain works, learning how to learn, etc. or learning ‘our T’ improves F; # Gaining knowledge improves F; # Exercise appears to maintain, strengthen and cause neurogenesis in our brains; # Nutrition; # And so on … Essentially the obvious stuff, so I am not going to expand on them too much in this chapter. The thing we can now do, however, thanks to the Wissner-Gross formula, is to understand in more depth how and why certain habits improve our intelligence or not. It is like knowing how to swim, but now you learn why you are able to swim (certain motions causes you to float or sink, oxygen is lighter than water, etc). Thanks to this understanding of intelligence and entropy, I am able to connect psychology, autism, and many more with these two. I even made chapters about them. For the video about Alexander Wissner-Gross talking about this formula: Practice So what one can do right now is to write down on a piece of paper or in a computer document all kinds of habits and see whether is improves your intelligence, F, or not, and also to try to find out whether it is mostly an improvement or reduction in the area of T (e.g. understanding your brain) or ∇ Sτ (certain habits that makes the future more secure like saving money). Category:Metacognition